Underwater Image Enhancement Based on Multi-Scale Attention and Contrast Learning

Yue Wang; Huijie Fan; Shiben Liu; Yandong Tang

doi:10.3788/LOP223047

Laser & Optoelectronics Progress, Volume. 61, Issue 4, 0437008(2024)

Underwater Image Enhancement Based on Multi-Scale Attention and Contrast Learning

Yue Wang^1,2,3, Huijie Fan^1,2、*, Shiben Liu^1,2,3, and Yandong Tang^1,2

¹State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China

²Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China

³University of Chinese Academy of Sciences, Beijing 100049, China

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Figures & Tables(11)

Fig. 1. Overall network structure

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Fig. 2. Multiscale channel pixel attention module

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Fig. 3. Feature matching experimental results. (a) Original image; (b) Fusion-based; (c) Water-Net; (d) Ucolor; (e) ours

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Fig. 4. Results of the standard color card recovery experiments. (a) Original image; (b) ULAP; (c) FUNIE-GAN; (d) Water-Net; (e) Shallow-UWnet; (f) Ucolor; (g) ours; (h) standard color card

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Fig. 5. Enhancement results of the different methods on UIEBD. (a) Original image; (b) UDCP; (c) Fusion-based; (d) ULAP; (e) Water-Net; (f) UWCNN; (g) FUNIE-GAN; (h) Shallow-UWnet; (i) Ucolor; (j) ours; (k) GT

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Fig. 6. Enhancement results of the different methods on EUVP1 and EUVP2. (a) Original image; (b) UDCP; (c) Fusion-based; (d) ULAP; (e)Water-Net; (f) UWCNN; (g) FUNIE-GAN; (h) Shallow-UWnet; (i) Ucolor; (j) ours; (k) GT

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Fig. 7. Enhancement results of the different methods on RUIE and HRCID . (a) Original image; (b) Water-Net; (c) UWCNN; (d) FUNIE-GAN; (e) Shallow-UWnet; (f) Ucolor; (g) ours

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Table 1. PSNR and SSIM values for the different methods on UIEBD, EUVP1 and EUVP2 datasets

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Table 1. PSNR and SSIM values for the different methods on UIEBD, EUVP1 and EUVP2 datasets

Method	UIEBD		EUVP1		EUVP2
Method	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
UDCP^［5］	11.13	0.4814	13.57	0.5222	12.31	0.3355
Fusion-based^［4］	20.66	0.8160	17.54	0.6003	19.53	0.5922
ULAP^［7］	16.63	0.6048	19.81	0.7334	17.14	0.5046
Water-Net^［9］	21.10	0.8689	18.70	0.7240	20.88	0.7168
UWCNN^［27］	12.93	0.4425	16.42	0.5907	17.88	0.6588
FUNIE-GAN^［14］	19.17	0.7728	24.38	0.8704	22.62	0.7250
Shallow-UWnet^［11］	18.27	0.7072	23.86	0.8375	22.40	0.6892
Ucolor^［10］	20.69	0.8031	23.96	0.8607	23.78	0.7699
Ours	23.83	0.8866	25.46	0.8946	24.39	0.7952

Table 2. Number of feature point matches for the different methods
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Table 2. Number of feature point matches for the different methods
Image No. Original image Fusion-based^［4］ Water-Net^［9］ Ucolor^［10］ Ours
Average 17.25 24.25 29.50 26.50 41.75
1 9 16 23 21 39
2 32 29 32 35 46
3 3 21 20 6 26
4 25 31 43 44 56

Table 3. UCIQE and entropy values for the different methods on RUIE and HRCID datasets

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Table 3. UCIQE and entropy values for the different methods on RUIE and HRCID datasets

Method	RUIE		HRCID
Method	UCIQE	Entropy	UCIQE	Entropy
Water-Net^［9］	0.5680	7.5092	0.5207	7.4918
UWCNN^［27］	0.4481	7.2463	0.4554	6.9498
FUNIE-GAN^［14］	0.5535	7.4984	0.5579	7.4125
Shallow-UWnet^［11］	0.3835	6.6503	0.3872	6.3581
Ucolor^［10］	0.5258	7.3653	0.4766	7.2015
Ours	0.5802	7.6668	0.5487	7.6461

Table 4. Results of the ablation experiments

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Table 4. Results of the ablation experiments

Model	PSNR	SSIM
Model 1	23.13	0.8711
Model 2	23.16	0.8824
Model 2（add Conv 7×7）	23.69	0.8847
Model 3	23.49	0.8835
Model 3（add Conv 5×5）	23.51	0.8845
Model 4	23.39	0.8845
Model 4（add Conv 3×3）	23.56	0.8846
Model 5	23.45	0.8796
Model 6	23.30	0.8783
Ours	23.83	0.8866

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Yue Wang, Huijie Fan, Shiben Liu, Yandong Tang. Underwater Image Enhancement Based on Multi-Scale Attention and Contrast Learning[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0437008

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